{"created":"2025-01-18T22:51:44.945023+00:00","updated":"2025-01-22T22:19:31.683200+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00019226","sets":["1164:1165:1184:1186"]},"path":["1186"],"owner":"1","recid":"19226","title":["時系列データからの多層ネットワーク特徴抽出手法の提案：Eigen Co - occurrence Matrix（ECM）"],"pubdate":{"attribute_name":"公開日","attribute_value":"2004-07-13"},"_buckets":{"deposit":"755fc328-dfc1-4159-8418-2f21c3751f12"},"_deposit":{"id":"19226","pid":{"type":"depid","value":"19226","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"時系列データからの多層ネットワーク特徴抽出手法の提案：Eigen Co - occurrence Matrix（ECM）","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"時系列データからの多層ネットワーク特徴抽出手法の提案：Eigen Co - occurrence Matrix（ECM）"},{"subitem_title":"Eigen Co - occurrence Matrix (ECM) : Method for Extracting Features of Sequential Data as Layered Networks","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2004-07-13","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"筑波大学大学院理工学研究科"},{"subitem_text_value":"筑波大学大学院理工学研究科"},{"subitem_text_value":"筑波大学システム情報工学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Master's Program in Science and Engineering at University of Tsukuba","subitem_text_language":"en"},{"subitem_text_value":"Master's Program in Science and Engineering at University of Tsukuba","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Systems and Information Engineering","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/19226/files/IPSJ-DBS04134012.pdf"},"date":[{"dateType":"Available","dateValue":"2006-07-13"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DBS04134012.pdf","filesize":[{"value":"279.8 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"13"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"7938131b-2277-4606-9eed-fc34527070a6","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2004 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"岡, 瑞起"},{"creatorName":"小磯, 知之"},{"creatorName":"加藤, 和彦"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Mizuki, Oka","creatorNameLang":"en"},{"creatorName":"Tomoyuki, Koiso","creatorNameLang":"en"},{"creatorName":"Kazuhiko, Kato","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10112482","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"コンピュータのセキュリティを考える上で重要な問題の1つに、有効なユーザに成りすますことによる不正行為を検知することが挙げられる．不正行為を検知する方法としては、異常検知によるアプローチが有効である．異常検知は、有効なユーザの挙動を学習することによりユーザのモデルを作成し、そのモデルから逸脱する挙動を異常と検知する．本稿では、異常検知に用いられる時系列データからのユーザの挙動の特徴抽出に着目し、Eigen Co-occurrence Matrix (ECM)手法という新たな時系列データからの特徴抽出手法を提案する．ユーザのUNIXコマンド時系列からECM手法を用いて特徴抽出を行い、異常検知に利用する．Schonlauらが提供するUNIXコマンドデータに対して成りすまし検知の実験を行った．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"One of the problems of importance in computer security is to detect the presence of an intruder masquerading as the valid user. Anomaly detection is a promising approach to detect intruders (masqueraders). Anomaly detection creates a user profile and labels any behavior that deviates from the profile as anomalous. A challenging task in detecting intruders is to model a user’s behavior based on sequential data, which can be used to effectively distinguish anomalous behaviors. In this paper, we propose a novel method, called the Eigen Cooccurrence Matrix (ECM), that models sequences of user actions (UNIX commands) and extracts their principal features. We applied ECM on the experiment of masquerade detection framed by Schonlau et al. We report the obtained result from the experiment and compare it with those from several conventional methods.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"90","bibliographic_titles":[{"bibliographic_title":"情報処理学会研究報告データベースシステム（DBS）"}],"bibliographicPageStart":"85","bibliographicIssueDates":{"bibliographicIssueDate":"2004-07-13","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"71(2004-DBS-134)","bibliographicVolumeNumber":"2004"}]},"relation_version_is_last":true,"weko_creator_id":"1"},"id":19226,"links":{}}